#!/usr/bin/env python3
"""
AI动画生成系统演示脚本
展示系统的基本功能和使用方法
"""

import os
import sys
import logging
from pathlib import Path
import torch
import numpy as np
from PIL import Image

# 设置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def create_demo_video():
    """创建演示视频"""
    logger.info("创建演示视频...")
    
    # 创建一个简单的动画视频
    frames = []
    width, height = 256, 256
    
    for i in range(24):  # 24帧，1秒动画
        # 创建一个渐变背景
        frame = np.zeros((height, width, 3), dtype=np.uint8)
        
        # 添加移动的圆形
        center_x = int(width // 2 + 50 * np.sin(i * 0.3))
        center_y = int(height // 2 + 30 * np.cos(i * 0.2))
        radius = 30
        
        # 绘制圆形
        y, x = np.ogrid[:height, :width]
        mask = (x - center_x)**2 + (y - center_y)**2 <= radius**2
        
        # 设置颜色
        color = [255, 100, 100]  # 红色
        frame[mask] = color
        
        # 添加渐变背景
        for y in range(height):
            for x in range(width):
                if not mask[y, x]:
                    intensity = int(100 + 50 * np.sin(x * 0.02 + i * 0.1))
                    frame[y, x] = [intensity, intensity, intensity + 50]
        
        frames.append(frame)
    
    return np.array(frames)

def save_demo_video(frames, output_path):
    """保存演示视频"""
    try:
        import imageio
        
        # 确保输出目录存在
        Path(output_path).parent.mkdir(parents=True, exist_ok=True)
        
        # 保存视频
        imageio.mimsave(output_path, frames, fps=24)
        logger.info(f"演示视频已保存: {output_path}")
        
    except ImportError:
        logger.warning("imageio未安装，无法保存视频")
        # 保存为图像序列
        output_dir = Path(output_path).parent / "demo_frames"
        output_dir.mkdir(parents=True, exist_ok=True)
        
        for i, frame in enumerate(frames):
            Image.fromarray(frame).save(output_dir / f"frame_{i:03d}.png")
        
        logger.info(f"演示帧已保存: {output_dir}")

def demo_text_processing():
    """演示文本处理功能"""
    logger.info("演示文本处理功能...")
    
    # 模拟文本处理器
    prompts = [
        "一只可爱的小猫在花园里玩耍",
        "小女孩在花海中跳舞",
        "宇航员在太空中漂浮",
        "日落时分的海滩波浪"
    ]
    
    styles = ["realistic", "cartoon", "anime", "watercolor"]
    
    print("\n=== 文本处理演示 ===")
    for prompt in prompts:
        print(f"原始提示: {prompt}")
        
        # 模拟文本增强
        enhanced = f"{prompt}，画面精美，动作自然流畅，高质量"
        print(f"增强提示: {enhanced}")
        
        # 模拟风格应用
        for style in styles:
            styled = f"{enhanced}, {style} style"
            print(f"  {style}: {styled}")
        
        print()

def demo_video_processing():
    """演示视频处理功能"""
    logger.info("演示视频处理功能...")
    
    print("\n=== 视频处理演示 ===")
    
    # 模拟视频信息
    video_info = {
        "resolution": "256x256",
        "fps": 24,
        "duration": 3.0,
        "frames": 72,
        "format": "MP4"
    }
    
    print("视频信息:")
    for key, value in video_info.items():
        print(f"  {key}: {value}")
    
    # 模拟处理操作
    operations = [
        "质量增强",
        "风格迁移 (卡通)",
        "视频稳定",
        "降噪处理"
    ]
    
    print("\n可用的处理操作:")
    for i, op in enumerate(operations, 1):
        print(f"  {i}. {op}")

def demo_model_architecture():
    """演示模型架构"""
    logger.info("演示模型架构...")
    
    print("\n=== 模型架构演示 ===")
    
    # 模拟模型信息
    models = {
        "Text2Video": {
            "type": "扩散模型",
            "backbone": "Stable Diffusion",
            "parameters": "1.5B",
            "input": "文本描述",
            "output": "视频序列"
        },
        "StyleTransfer": {
            "type": "风格迁移",
            "backbone": "Neural Style Transfer",
            "parameters": "500M",
            "input": "视频 + 风格",
            "output": "风格化视频"
        },
        "VideoEditing": {
            "type": "视频编辑",
            "backbone": "Video-LLaMA",
            "parameters": "2B",
            "input": "视频 + 指令",
            "output": "编辑后视频"
        }
    }
    
    for model_name, info in models.items():
        print(f"\n{model_name} 模型:")
        for key, value in info.items():
            print(f"  {key}: {value}")

def demo_usage_examples():
    """演示使用示例"""
    logger.info("演示使用示例...")
    
    print("\n=== 使用示例 ===")
    
    examples = [
        {
            "功能": "文本生成动画",
            "命令": "python main.py generate --prompt '一只可爱的小猫在花园里玩耍' --style cartoon",
            "描述": "从文本描述生成卡通风格的动画"
        },
        {
            "功能": "风格迁移",
            "命令": "python main.py style-transfer --input video.mp4 --style anime",
            "描述": "将视频转换为动漫风格"
        },
        {
            "功能": "视频编辑",
            "命令": "python main.py edit --input video.mp4 --edit-prompt '增强视频质量'",
            "描述": "使用AI指令编辑视频"
        },
        {
            "功能": "批量处理",
            "命令": "python main.py batch --input-dir ./input --output-dir ./output --style cartoon",
            "描述": "批量处理多个视频文件"
        }
    ]
    
    for example in examples:
        print(f"\n{example['功能']}:")
        print(f"  命令: {example['命令']}")
        print(f"  描述: {example['描述']}")

def demo_web_interface():
    """演示Web界面"""
    logger.info("演示Web界面...")
    
    print("\n=== Web界面演示 ===")
    
    print("启动Web界面:")
    print("  python app/gradio_app.py")
    print("\n访问地址: http://localhost:7860")
    
    print("\n界面功能:")
    print("  🎨 文本生成动画 - 从文本描述生成动画")
    print("  🎭 风格迁移 - 应用不同艺术风格")
    print("  ✂️ 视频编辑 - 智能视频编辑")
    print("  📦 批量处理 - 批量处理多个文件")

def main():
    """主演示函数"""
    print("🎬 AI动画生成系统演示")
    print("=" * 50)
    
    try:
        # 检查环境
        print("检查环境...")
        print(f"Python版本: {sys.version}")
        print(f"PyTorch版本: {torch.__version__}")
        print(f"CUDA可用: {torch.cuda.is_available()}")
        
        if torch.cuda.is_available():
            print(f"GPU数量: {torch.cuda.device_count()}")
            print(f"GPU名称: {torch.cuda.get_device_name(0)}")
        
        # 创建演示视频
        frames = create_demo_video()
        save_demo_video(frames, "outputs/videos/demo_animation.mp4")
        
        # 演示各项功能
        demo_text_processing()
        demo_video_processing()
        demo_model_architecture()
        demo_usage_examples()
        demo_web_interface()
        
        print("\n" + "=" * 50)
        print("🎉 演示完成！")
        print("\n下一步:")
        print("1. 激活环境: conda activate animation-ai")
        print("2. 启动Web界面: python app/gradio_app.py")
        print("3. 开始创作你的动画！")
        
    except Exception as e:
        logger.error(f"演示过程中出现错误: {e}")
        print(f"\n❌ 错误: {e}")
        print("请检查环境设置是否正确")

if __name__ == "__main__":
    main() 